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<table width="100%" summary="page for WomenQueue"><tr><td>WomenQueue</td><td align="right">R Documentation</td></tr></table>

<h2>Women in Queues</h2>

<h3>Description</h3>


<p>Data from Jinkinson \&amp; Slater (1981) and Hoaglin \&amp; Tukey (1985)
reporting the frequency distribution of females in 100 queues of
length 10 in a London Underground station.
</p>


<h3>Usage</h3>

<pre>
data("WomenQueue")
</pre>


<h3>Format</h3>


<p>A 1-way table giving the number of women in 100 queues of length
10.  The variable and its levels are 
</p>

<table summary="Rd table">
<tr>
 <td align="right">
    No </td><td align="left"> Name </td><td align="left"> Levels </td>
</tr>
<tr>
 <td align="right">
    1 </td><td align="left"> nWomen </td><td align="left"> 0, 1, ..., 10 </td>
</tr>
<tr>
 <td align="right">
  </td>
</tr>

</table>



<h3>Source</h3>


<p>M. Friendly (2000),
Visualizing Categorical Data,
pages 19&ndash;20.
</p>


<h3>References</h3>


<p>D. C. Hoaglin \&amp; J. W. Tukey (1985),
Checking the shape of discrete distributions.
In D. C. Hoaglin, F. Mosteller, J. W. Tukey (eds.),
<EM>Exploring Data Tables, Trends and Shapes</EM>, chapter 9.
John Wiley \&amp; Sons, New York.
</p>
<p>R. A. Jinkinson \&amp; M. Slater (1981),
Critical discussion of a graphical method for identifying discrete
distributions,
<EM>The Statistician</EM>, <B>30</B>, 239&ndash;248.
</p>
<p>M. Friendly (2000),
<EM>Visualizing Categorical Data</EM>.
SAS Institute, Cary, NC.
</p>


<h3>Examples</h3>

<pre>
data("WomenQueue")
gf &lt;- goodfit(WomenQueue, type = "binomial")
summary(gf)
plot(gf)
</pre>


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